We offer one PhD scholarship in socially aware artificial intelligence for future transportation. The PhD project aims to develop and test a new generation of mathematical models for explaining and predicting human behavior in urban environments in interactions with autonomous future transportation technologies such as self-driving cars and last-mile delivery robots.
In the project, you will first formulate new mathematical models for explaining and predicting the choices of pedestrians and cyclists during interactions with autonomous transportation technologies by integrating methods from econometrics and cognitive psychology. Then, you will design, implement and conduct virtual reality experiments to apply the proposed models.
The project is a strategic collaboration between the Technical University of Denmark (DTU) and its alliance partner, the Norwegian University of Science and Technology (NTNU).
The PhD study is under the DTU-NTNU double degree framework agreement, which offers the opportunity to receive PhD diplomas from both DTU (home institution) and NTNU (host institution).
You will be a member of the Machine Learning for Smart Mobility section of the Transport division at the Department for Technology, Management and Economics (DTU Management) at the Technical University of Denmark (DTU). You will work under the supervision of Professor Francisco Pereira and Assistant Professor Rico Krueger. The virtual reality experiments will be designed, implemented and conducted under the supervision of Associate Professor Xiang Su from the Department of Computer Science at the Norwegian University of Science and Technology (NTNU).
Responsibilities and qualifications
Your primary tasks will be to
- Develop mathematical models for explaining and predicting the behaviors of pedestrians and cyclists in interactions with autonomous transportation technologies such as self-driving vehicles and last-mile delivery robots,
- Formulate and test estimators for parameter inference in the proposed models using advanced statistical and computational approaches, including Bayesian methods and other simulation-assisted techniques,
- Design, implement and conduct immersive and augmented virtual reality experiments using state-of-the-art soft- and hardware,
- Analyse the collected data using the newly developed models and other state-of-the-art methods,
- Write academic papers aimed at high-impact journals,
- Participate in international conferences and workshops,
- Disseminate research results and teach as part of the overall PhD education.
You must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree. Specifically, we seek applicants with a master’s degree in transport, mathematics, statistics, computer science, economics, psychology, human factors, civil engineering, industrial engineering or a related discipline.
We are looking for an ambitious, self-organized individual with strong project management and communication skills. Applicants should have experience in some of the following areas: mathematical modelling, statistics, machine learning, artificial intelligence, design and implementation of virtual reality experiments, data collection and analysis. Programming skills in Python, Julia, R or similar and proficient English language skills are also required.
Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programs of DTU and NTNU. For information about the general requirements for enrollment and the general planning of the scholarship studies, please see the DTU Guide and NTNU PhD Guide.
Assessment
The review of applications will begin on 8 September 2022.
We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.
Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years. The starting date is 1 January 2023.
You can read more about career paths at DTU here.
Workplace
This PhD study is under the Double Doctorate Degree (cotutelle) agreement between DTU and NTNU. DTU will be the home institution that will handle all the administrative and financial aspects of the joint education. DTU will enroll the candidate in one of its PhD programs and nominate the main supervisor. NTNU will be the host institution and will also enroll the candidate in one of its PhD programs. The candidate must spend a minimum of one year at each of the two institutions.
Further information
Further information about the position may be obtained from Assistant Professor Rico Krueger (rickr@dtu.dk).
You can read more about DTU Management at www.man.dtu.dk/english.
If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU.
Application procedure
Your complete online application must be submitted no later than 7 September 2022 (Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link “Apply online”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:
- A letter motivating the application (cover letter)
- Curriculum vitae
- Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale
You may apply prior to obtaining your master’s degree but cannot begin before having received it.
Applications received after the deadline will not be considered.
All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.
About the department
The Machine Learning for Smart Mobility (MLSM) group belongs to the Transport division of the Department of Technology, Management and Economics (DTU Management) at DTU. The division conducts research and teaching in the field of traffic and transport planning, with particular focus on behavior modelling, machine learning and simulation.
DTU Management conducts high-level research and teaching with a focus on sustainability, transport, innovation, and management science. Our goal is to create knowledge on the societal aspects of technology – including the interaction between technology and sustainability, business growth, infrastructure, and prosperity. Therefore, we explore and create value in the areas of management science, innovation and design thinking, business analytics, systems and risk analyses, human behavior, regulation and policy analysis. The department offers teaching from introductory to advanced courses/projects at BSc, MSc and PhD level. The Department has a staff of approximately 350 people.
Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 13,400 students and 5,800 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.
Apply for this job
Apply no later than 7 September 2022
Apply for the job at DTU Management by completing the following form.